System and Method for Verifiable Integrity Assessment of Physical Objects in a Commercial Transaction Lifecycle

The present disclosure relates generally to e-commerce integrity and trust systems, and more particularly to systems and methods for securely capturing, analyzing, and comparing condition-related data of physical objects during commercial transactions, such as product listings, deliveries, and returns, using AI-driven verification technologies.

With the rapid growth of e-commerce and recommerce (resale) marketplaces, users increasingly rely on digital platforms to buy, sell, and return physical goods without inspecting them in person. While this convenience has fueled market expansion, it has also introduced significant challenges related to trust, transparency, and fraud.

A persistent issue in online transactions is subjective or misleading representations of product condition. Sellers may upload outdated, altered, or stock images that do not reflect the actual item being shipped. This leads to disputes and dissatisfaction from buyers who receive products that differ from their expectations.

Moreover, return fraud has become a major concern. Common tactics include item swapping, false defect claims, and seller-side deception. The item swapping, where the buyer returns a different or damaged item than the one originally received. The false defect claims, where buyers intentionally damage products and claim they were received in that condition. The seller-side deception, where sellers misrepresent defective or damaged items using staged photography. These problems result in substantial losses for platforms and sellers, erode buyer trust, and overload customer service and fraud resolution teams.

To mitigate these issues, various technologies have been employed. Many platforms depend on human graders or reviewers to inspect and describe item condition before shipment. However, this method is subjective, labor-intensive, and lacks scalability. Users are prompted to upload photos during listing or return. While simple, this approach does not ensure that the images are authentic, recent, or even correspond to the item being listed or returned. Some systems track items using unique identifiers like barcodes or serial numbers. However, this method does not verify physical condition or prevent fraud through visual tampering.

Emerging platforms have started incorporating AI to assess cosmetic defects. These systems, however, often lack real-time guidance, multi-point verification, or comparative analysis capabilities between multiple capture events. Limited technologies exist for digitally signing image metadata to verify time and origin, but these often require intrusive permissions or are not tamper-proof at the user level.

Overall, existing systems fall short in offering a holistic, automated, and tamper-resistant method for capturing and verifying object integrity throughout a product's transaction lifecycle.

Therefore, there is a need for systems and methods for securely capturing, analyzing, and comparing condition-related data of physical objects during commercial transactions, such as product listings, deliveries, and returns, using AI-driven verification technologies.

The following presents a simplified summary of one or more embodiments of the present disclosure to provide a basic understanding of such embodiments. This summary is not an extensive overview of all contemplated embodiments and is intended to neither identify key nor critical elements of all embodiments, nor delineate the scope of any or all embodiments.

The present disclosure, in one or more embodiments, relates to a system for verifiable integrity assessment of physical objects in e-commerce transactions. The system comprises a computing device having a processor, a capturing unit, at least one sensor, and a memory for storing one or more instructions executable by the processor.

An embodiment of the first aspect, wherein the processor is configured to guide a user to capture visual data that comprises at least one of images, and videos of a physical object, and a unique identifier. The unique identifier comprises at least one of an international mobile equipment identity (IMEI), serial number, global trade item number (GTIN), or manufacturer part number (MPN) visible on the physical object. The processor is configured to obtain non-visual data that comprises at least one of motion data, device attestation signals, and a timestamp from a trusted source or a time-stamping authority.

The computing device obtains the timestamp from a network time protocol (NTP) server or a cryptographic timestamp from a time stamping authority (TSA). The processor is configured to generate at least one verifiable data package, which comprises the visual data, and the non-visual data. The computing device obtains location information, which comprises GPS coordinates or IP-derived location, and is stored as part of the verifiable data package for contextual fraud risk assessment.

An embodiment of the first aspect, wherein the processor is configured to perform a real-time quality check on the at least one verifiable data package using at least one AI model. The real-time quality check is performed using a cascade of artificial intelligence (AI) models that provide capture guidance and automatic capture triggering. The processor is configured to initiate a one-time device registration process by generating a cryptographic key pair and initiate a certificate signing request.

An embodiment of the first aspect, the system comprises a backend server that is in communication with the computing device via a network. The backend server comprises an API gateway module, a certificate authority (CA) module, a backend processing module, and a comparative analysis module.

An embodiment of the first aspect, wherein the API gateway module is configured to receive the verifiable data package and the certificate signing request from the computing device. The CA module is configured to issue a client certificate based on the certificate signing request. The client certificate is stored in the memory. The backend processing module is configured to process a device registration request and analyze the verifiable data package using artificial intelligence (AI) models to generate analysis data. The analysis data comprise generating flaw maps, condition grades, natural language descriptions, and confidence scores. The comparative analysis module is configured to compare two verifiable data packages associated with the same transaction. The backend server comprises a database that is configured to store the verifiable data package, analysis results, and certificate information

An embodiment of the first aspect, the system comprises a customer server that is configured to authenticate the computing device and retrieve analysis results from the backend server, and selectively initiate fraud resolution processes

An embodiment of the first aspect, wherein a method for verifiable integrity assessment of a physical object in a commercial transaction. At first, a cryptographic key pair is generated by the processor and a certificate signing request is submitted to the backend server. Next, the client certificate is received by the backend server from the CA module and stored in the memory in the computing device.

Next, a first verifiable data package is captured by the capturing unit i.e., a camera of the computing device. The first verifiable data package comprises images or videos of the physical object, a unique identifier associated with the physical object, and non-visual data including sensor data and device attestation signals. Next, the first verifiable data package is transmitted via the network to the backend server. The backend processing module generates an enhanced version of the images by segmenting the physical object from a background for visual clarity.

Next, the first verifiable data package is analysed using the artificial intelligence (AI) models to generate analysis data. Later, the analysis data is stored in the database and generates a verification report accessible to the customer server. The analysis data comprise generating flaw maps, condition grades, natural language descriptions, and confidence scores.

An embodiment of a second aspect, wherein a method for comparative integrity analysis of the physical object during a commercial transaction lifecycle. At first, a first verifiable data package is received during a product listing event. Next, a second verifiable data package is received during a return event. Next, unique identifiers are extracted by the backend processing module from the first verifiable data package, and the second verifiable data package. The comparative analysis module compares the extracted unique identifiers using artificial intelligence (AI) models to generate analysis data.

Next, the analysis data, derived from the first verifiable data package, and the second verifiable data package, is generated and compared by the comparative analysis module. Next, inconsistencies or mismatches in at least one of the unique identifiers, and the analysis data are flagged by the computing device. Later, a consistency report for adjudication of return or fraud assessment is generated. The backend processing module is configured to generate a confidence score and used to determine whether the consistency report requires manual analyst review. The consistency report is transmitted to an analyst portal for manual verification and adjudication.

While multiple embodiments are disclosed, still other embodiments of the present disclosure will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the invention. As will be realized, the various embodiments of the present disclosure are capable of modifications in various obvious aspects, all without departing from the spirit and scope of the present disclosure. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.

Reference will now be made in detail to the present preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

refers to a block diagram of a systemfor verifiable integrity assessment of physical objects in e-commerce transactions. The systemcomprises a computing devicehaving a processor, a capturing unit, at least one sensor, and a memoryfor storing one or more instructions executable by the processor.

In one embodiment herein, the systemcomprises the computing devicehaving the processorand the memory, which stores one or more instructions executable by the processor. These instructions may be executed to cause the systemto perform the various functionalities. The processoracts as the central processing unit (CPU) of the system, responsible for coordinating different tasks and carrying out complex operations, data processing, and decision-making by fetching instructions from the memory, thereby decoding the instructions and executing the necessary actions.

In one embodiment herein, the memoryserves as the storage component of the system, holding the executable instructions, as well as any data or information required by the processorto perform its tasks. The data includes user inputs, system configurations, and any other relevant data needed for the system's operations. Through the communication between the processorand the memory, the systemis able to process the user inputs, access stored information, perform computations, and make decisions accordingly.

In one embodiment, the memoryis at least one of a secure hardware-backed storage, and a platform-native secure hardware storage.

In some embodiment, the memoryis a non-transitory computer-readable medium or non-transitory refers to computer-readable media that stores data for short periods or in the presence of power such as random-access memory.

In one embodiment herein, the computing devicerepresents any electronic device that the user can utilize to interact with the system. The computing devicecan be, but not limited to, a smartphone, a laptop, a tablet, a personal computer, or any other suitable electronic device. The computing deviceserves as the user's gateway to accessing and interacting with the system. The computing deviceis configured to enable the user to engage with the system's functionalities and capabilities through a user interface.

In one embodiment herein, the user interfaceis a crucial component of the computing device, which allows the users to input commands, receive information, and control the system. The user interfacecan be, but not limited to, a touch screen, a keyboard, a mouse, voice recognition modules, gesture recognition sensors, and virtual reality interfaces. The versatility of the user interfaceensures that the users can engage with the systemin a manner that is most intuitive and comfortable for the users, thereby catering to a wide range of user preferences and accessibility needs. The computing deviceempowers the users to interact with the systemseamlessly and efficiently by providing multiple user interface options, thereby leveraging the most appropriate input and output modalities for their specific needs and preferences.

In one embodiment herein, the computing deviceis in communication with a backend server, a customer serverand a databasevia a network. The networkacts as a communication that allows the computing deviceto interact with the other components of the system, thereby facilitating the exchange of data, commands, and information. In one embodiment herein, the networkcan be a wireless communication infrastructure, which offers the users flexibility and convenience when interacting with the system. This wireless connectivity enables the users to access the systemfrom various locations, without being tethered to a fixed physical connection.

In one embodiment herein, the networkcan be, but not limited to, Local Area Network (LAN), Cellular Network, Wide Area Network (WAN), Intranet, Virtual Private Network (VPN), and wireless networks that use radio frequency (RF) or infrared (IR) technology to transmit data without the need for physical cables, thereby providing mobility and flexibility. The versatility of the networkensures that the computing devicecan seamlessly connect to the backend serverand the database, thereby enabling the users to access the system'sfunctionalities and resources from a variety of locations and devices. This wireless connectivity enhances the overall accessibility and convenience of the systemfor the users.

In one embodiment, a client-side software development kit (SDK) is embedded within a customer application, which is executable on the processorof the computing device. The customer application comprises a mobile or web application. The client-side SDK is a collection of software tools, libraries, and APIs that are embedded into a mobile or web application running on the computing device. The SDK allows developers to add specific functionalities such as image capture, data encryption, or AI processing-without having to build them from scratch.

In one embodiment, the processoris configured to guide a user to capture visual data that comprises at least one of images, and videos of a physical object, and a unique identifier. The unique identifier comprises at least one of an international mobile equipment identity (IMEI), serial number, global trade item number (GTIN), or manufacturer part number (MPN) visible on the physical object. The processoris configured to obtain non-visual data that comprises at least one of motion data, device attestation signals, and a timestamp from a trusted source or a time-stamping authority. The computing deviceobtains the timestamp from a network time protocol (NTP) server or a cryptographic timestamp from a time stamping authority (TSA). The processoris configured to generate at least one verifiable data package, which comprises the visual data, and the non-visual data. The computing deviceobtains location information, which comprises GPS coordinates or IP-derived location, and is stored as part of the verifiable data package for contextual fraud risk assessment.

In one embodiment, the processoris configured to perform a real-time quality check on at least one verifiable data package using at least one AI model. The real-time quality check in the SDK is performed using a cascade of artificial intelligence (AI) models that provide capture guidance and automatic capture triggering. The processoris configured to initiate a one-time device registration process by generating a cryptographic key pair and initiate a certificate signing request. The cryptographic key pair comprises private key and public key.

In one embodiment, the systemcomprises a backend serveris in communication with the computing devicevia the network. The backend servercomprises an API gateway module, a certificate authority (CA) module, a backend processing module, and a comparative analysis module.

In one embodiment, the API gateway moduleis configured to receive the verifiable data package and the certificate signing request from the computing device. The CA moduleis configured to issue a client certificate based on the certificate signing request. The client certificate is stored in the platform-native secure hardware storage such as an Android Keystore or iOS Secure Enclave.

In one embodiment, the CA modulecomprises certificate authority (CA) is a trusted entity that issues digital certificates-specifically, public key certificates-to verify the identity of users, devices, or software and enable secure communication over the internet or private networks.

In one embodiment, the backend processing moduleis configured to process a device registration request and analyze the verifiable data package using artificial intelligence (AI) models to generate analysis data. The analysis data comprise generating flaw maps, condition grades, natural language descriptions, and confidence scores. The comparative analysis moduleis configured to compare two verifiable data packages associated with the same transaction. The backend servercomprises the databasethat is configured to store the verifiable data package, analysis results, and certificate information

In one embodiment, the customer serveris configured to authenticate the computing deviceand retrieve analysis results from the backend server, and selectively initiate fraud resolution processes

In a preferred embodiment, the SDK is a secure, tamper-resistant software module integrated into the customer application. The SDK, which is executable on the processorof the computing device, guides the user in capturing a plurality of images and videos of the physical object. Further the SDK uses on-device AI model to analyze the captured image quality in real time, collects metadata such as sensor readings, timestamps, GPS location, and encrypts, and transmits data securely to the backend server.

In a preferred embodiment, when the SDK is first installed and initialized on the processorof the computing device. The SDK generates the cryptographic key pair. The SDK creates a certificate signing request (CSR) using the public key and device details.

The CA modulein the backend servervalidates the CSR (e.g., using a secure token), issues and signs a client certificate using its private key, and sends the signed certificate back to the SDK, which stores it securely. The SDK then uses that certificate for mutual TLS (mTLS) communication with the backend server. This ensures that the backend serverknows it's communicating with a legitimate, registered device, and the device knows it's talking to the backend server.

In a preferred embodiment, the processoris configured to initiate a one-time device registration process by generating the cryptographic key pair and initiate the certificate signing request. The cryptographic key pair comprises private key and public key. The processorperforms a multi-stage initialization and communication security process that establishes a unique, hardware-bound identity for each instance of the SDK and enables mutually authenticated, secure communication with the backend server. This process consists of, an initial brokered authentication via the customer server, a one-time mutual TLS (mTLS)-based device registration, and ongoing secure data exchange using the issued certificate. The customer serverplays a broker role in authenticating the client-side SDK before backend registration. The authentication token or credentials are validated by the backend serverprior to certificate issuance

In an exemplary embodiment, during first-time SDK initialization (or reinitialization following certificate expiry), the client-side SDK requires a temporary authentication token to register securely with the backend. The client-side SDK is embedded within the customer application, which is executable on the processorof the computing device. To avoid embedding long-lived credentials within the customer application a brokered authentication flow is employed. The processorinitiates a request to the customer server. The customer server, which securely stores a long-lived API key or secret, forwards a request to the Backend Server's authentication endpoint. The Backend Server validates the credentials and issues a short-lived, single-use secure token (e.g., a JWT), which is returned to the customer server. This token is passed to the processorand provided to the SDK to initiate registration.

Upon receiving the secure token, the processorproceeds with device registration. A cryptographic key pair is generated locally on the computing device. The private key is stored in a hardware-backed secure element, such as the Android Keystore or iOS Secure Enclave. A certificate signing request (CSR), containing the public key and device-specific metadata, is created by the processor. The CSR is transmitted to the API Gateway moduleof the backend server, authenticated using the previously issued token. The CA moduleof the backend servervalidates the token, processes the CSR, and acts as a private Certificate Authority (CA) to generate and sign a unique client certificate. The signed client certificate is returned to the processorand stored securely on the computing device.

Further, using the stored client certificate and private key, the SDK initiates an mTLS handshake with the API Gateway module. This ensues bidirectional authentication, where the processorverifies the backend serververifies the specific SDK instance. All subsequent interactions, including verifiable data package uploads and retrieval of analysis results, occur over this secure channel, without reusing the brokered token.

Once the mTLS session is established, the processorguides the user through the verifiable data capture process. The captured data, including visual and non-visual components, is packaged and securely transmitted to the backend serverover the mTLS channel. The backend serverreceives and stores the verifiable data package and forwards it to the backend processing module. The backend processing moduleanalyzes the package to generate flaw maps, condition grades, and confidence scores, which are stored in the database. The backend servermakes the analysis results accessible to the computing deviceand the customer serverthrough API endpoints or webhook notifications.

In one embodiment, the systemcomprises is configured for business-to-business (B2B) inventory verification, wherein a receiving entity-such as a professional reseller or warehouse operator-validates the condition of incoming physical goods against a supplier-provided digital manifest. The systemreceives the manifest, which includes a list of items along with their expected condition grades. Upon intake, each item is scanned using the client-side SDK embedded in the customer application, which is executable on the processorof the computing device. The client-side SDK guides the user through a verifiable capture process and transmits the captured data to the backend. The backend processing moduleanalyzes the visual and non-visual data to generate an objective condition grade for each item. These Al-generated grades are then automatically compared with the corresponding grades listed in the supplier's manifest. Based on this comparison, the systemgenerates an intake discrepancy report, highlighting mismatches and providing verifiable evidence for dispute resolution or quality control.

refers to a flowchartof a method for verifiable integrity assessment of a physical object in a commercial transaction. At step, a cryptographic key pair is generated by the processorand the certificate signing request is submitted to the backend server. At step, the client certificate is received by the backend serverfrom the CA moduleand stored the client certificate in the memory.